# Image Amodal Completion: A Survey

arXiv: https://arxiv.org/abs/2207.02062 · HTML: https://mlsanity.com/paper/2207.02062/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 75 of its 104 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 30 of 75)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [Joint 2D-3D-Semantic Data for Indoor Scene Understanding](https://mlsanity.com/paper/1702.01105/) (2017)
- [MONet: Unsupervised Scene Decomposition and Representation](https://mlsanity.com/paper/1901.11390/) (2019)
- [Multi-Object Representation Learning with Iterative Variational Inference](https://mlsanity.com/paper/1903.00450/) (2019)
- [Single-View View Synthesis with Multiplane Images](https://mlsanity.com/paper/2004.11364/) (2020)
- [GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations](https://mlsanity.com/paper/1907.13052/) (2019)
- [Pluralistic Image Completion](https://mlsanity.com/paper/1903.04227/) (2019)
- [Fully Convolutional Instance-aware Semantic Segmentation](https://mlsanity.com/paper/1611.07709/) (2016)
- [Deep Image Harmonization](https://mlsanity.com/paper/1703.00069/) (2017)
- [Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing](https://mlsanity.com/paper/1703.05446/) (2017)
- [Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers](https://mlsanity.com/paper/2103.12340/) (2021)

## Built on by (most cited first, 18 of 18)

- [MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation](https://mlsanity.com/paper/2404.02790/) (2024)
- [Open-World Amodal Appearance Completion](https://mlsanity.com/paper/2411.13019/) (2024)
- [PISCO: Precise Video Instance Insertion with Sparse Control](https://mlsanity.com/paper/2602.08277/) (2026)
- [Amodal Optical Flow](https://mlsanity.com/paper/2311.07761/) (2023)
- [Occlusion-Aware Seamless Segmentation](https://mlsanity.com/paper/2407.02182/) (2024)
- [Occ2Net: Robust Image Matching Based on 3D Occupancy Estimation for Occluded Regions](https://mlsanity.com/paper/2308.16160/) (2023)
- [Amodal Depth Anything: Amodal Depth Estimation in the Wild](https://mlsanity.com/paper/2412.02336/) (2024)
- [PLUG: Revisiting Amodal Segmentation with Foundation Model and Hierarchical Focus](https://mlsanity.com/paper/2405.16094/) (2024)
- [SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop Scenes](https://mlsanity.com/paper/2307.07333/) (2023)
- [DMAT: A Dynamic Mask-Aware Transformer for Human De-occlusion](https://mlsanity.com/paper/2402.04558/) (2024)
- [Foundation Models for Amodal Video Instance Segmentation in Automated Driving](https://mlsanity.com/paper/2409.14095/) (2024)
- [GPT-4 for Occlusion Order Recovery](https://mlsanity.com/paper/2509.22383/) (2025)
- [Reasoning-Driven Amodal Completion: Collaborative Agents and Perceptual Evaluation](https://mlsanity.com/paper/2512.20936/) (2025)
- [Mask Guided Gated Convolution for Amodal Content Completion](https://mlsanity.com/paper/2407.15203/) (2024)
- [Track Anything Behind Everything: Zero-Shot Amodal Video Object Segmentation](https://mlsanity.com/paper/2411.19210/) (2024)
- [Bridging Perception and Language: A Systematic Benchmark for LVLMs' Understanding of Amodal Completion Reports](https://mlsanity.com/paper/2507.05799/) (2025)
- [A solution to generalized learning from small training sets found in infants repeated visual experiences of individual objects](https://mlsanity.com/paper/2510.15060/) (2025)
- [SemLayer: Semantic-aware Generative Segmentation and Layer Construction for Abstract Icons](https://mlsanity.com/paper/2603.24039/) (2026)

## Abstract (arXiv)

Existing computer vision systems can compete with humans in understanding the visible parts of objects, but still fall far short of humans when it comes to depicting the invisible parts of partially occluded objects. Image amodal completion aims to equip computers with human-like amodal completion functions to understand an intact object despite it being partially occluded. The main purpose of this survey is to provide an intuitive understanding of the research hotspots, key technologies and future trends in the field of image amodal completion. Firstly, we present a comprehensive review of the latest literature in this emerging field, exploring three key tasks in image amodal completion, including amodal shape completion, amodal appearance completion, and order perception. Then we examine popular datasets related to image amodal completion along with their common data collection methods and evaluation metrics. Finally, we discuss real-world applications and future research directions for image amodal completion, facilitating the reader's understanding of the challenges of existing technologies and upcoming research trends.